Retinal Vessel Segmentation On Drive

Métriques

AUC

F1 score

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	AUC	F1 score	Paper Title	Repository
IterNet	0.9816	0.8205	IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks
ConvMixer	-	0.8245	Deep Learning Architectures for Diagnosis of Diabetic Retinopathy
BCDU-Net (d=3)	0.9789	0.8224	Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions
Study Group Learning	0.9886	0.8316	Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels
ConvMixer-Light	-	0.8215	Deep Learning Architectures for Diagnosis of Diabetic Retinopathy
ET-Net	-	-	ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image Segmentation
MERIT-GCASCADE	-	0.8290	G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation
U-Net	0.9755	0.8142	U-Net: Convolutional Networks for Biomedical Image Segmentation
SA-UNet	0.9864	0.8263	SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation
RV-GAN	-	-	RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scale Generative Adversarial Network
VGN	0.9802	0.8263	Deep Vessel Segmentation By Learning Graphical Connectivity
U-Net	0.9855	-	Exploring The Limits Of Data Augmentation For Retinal Vessel Segmentation
PVT-GCASCADE	-	0.8210	G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation
FR-UNet	0.9889	0.8316	Full-Resolution Network and Dual-Threshold Iteration for Retinal Vessel and Coronary Angiograph Segmentation
DUNet	0.9802	0.8237	DUNet: A deformable network for retinal vessel segmentation	-
FSG-Net	0.9823	0.8322	Full-scale Representation Guided Network for Retinal Vessel Segmentation
LadderNet	0.9793	0.8202	LadderNet: Multi-path networks based on U-Net for medical image segmentation
CE-Net	0.9779	-	CE-Net: Context Encoder Network for 2D Medical Image Segmentation
Swin-Res-Net	0.9931	-	Enhancing Retinal Vascular Structure Segmentation in Images With a Novel Design Two-Path Interactive Fusion Module Model
Residual U-Net	0.9779	0.8149	Road Extraction by Deep Residual U-Net

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